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Articles

A generative approach for semantic auditing of electronic health records

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By Moni Shahar, Ran Gilad-Bachrach, Irena Girshovitz, Atai Ambus
| Nature Verified
Abstract The reliability of clinical artificial intelligence (AI) depends on high-quality data, yet Electronic Health Records are often inconsistent with existing scientific knowledge. Current quality assessments are limited: they either focus on syntax or rely on labor-intensive manual rules to capture semantic nuances. To overcome these scalability barriers, we propose Medical Data Pecking, a methodology that adopts software unit testing principles for medical data validation.
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